Hi – I'd like to follow up on this, as I am running into very similar issues (with a much bigger data set, though – 10^5 nodes, 10^7 edges).
So let me repost the question: Any ideas on how to estimate graphx memory requirements? Cheers! Von: Roman Sokolov [mailto:ole...@gmail.com] Gesendet: Samstag, 11. Juli 2015 03:58 An: Ted Yu; Robin East; user Betreff: Re: Spark GraphX memory requirements + java.lang.OutOfMemoryError: GC overhead limit exceeded Hello again. So I could compute triangle numbers when run the code from spark shell without workers (with --driver-memory 15g option), but with workers I have errors. So I run spark shell: ./bin/spark-shell --master spark://192.168.0.31:7077<http://192.168.0.31:7077> --executor-memory 6900m --driver-memory 15g and workers (by hands): ./bin/spark-class org.apache.spark.deploy.worker.Worker spark://192.168.0.31:7077<http://192.168.0.31:7077> (2 workers, each has 8Gb RAM; master has 32 Gb RAM). The code now is: import org.apache.spark._ import org.apache.spark.graphx._ val graph = GraphLoader.edgeListFile(sc, "/home/data/graph.txt").partitionBy(PartitionStrategy.RandomVertexCut) val newgraph = graph.convertToCanonicalEdges() val triangleNum = newgraph.triangleCount().vertices.map(x => x._2.toLong).reduce(_ + _)/3 So how to understand what amount of memory is needed? And why I need it so much? Dataset is only 1,1Gb small... Error: [Stage 7:> (0 + 8) / 32]15/07/11 01:48:45 WARN TaskSetManager: Lost task 2.0 in stage 7.0 (TID 130, 192.168.0.28): io.netty.handler.codec.DecoderException: java.lang.OutOfMemoryError at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:153) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:333) at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:319) at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:787) at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:130) at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511) at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468) at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382) at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354) at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116) at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.OutOfMemoryError at sun.misc.Unsafe.allocateMemory(Native Method) at java.nio.DirectByteBuffer.<init>(DirectByteBuffer.java:127) at java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:306) at io.netty.buffer.PoolArena$DirectArena.newUnpooledChunk(PoolArena.java:440) at io.netty.buffer.PoolArena.allocateHuge(PoolArena.java:187) at io.netty.buffer.PoolArena.allocate(PoolArena.java:165) at io.netty.buffer.PoolArena.reallocate(PoolArena.java:277) at io.netty.buffer.PooledByteBuf.capacity(PooledByteBuf.java:108) at io.netty.buffer.AbstractByteBuf.ensureWritable(AbstractByteBuf.java:251) at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:849) at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:841) at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:831) at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:146) ... 10 more On 26 June 2015 at 14:06, Roman Sokolov <ole...@gmail.com<mailto:ole...@gmail.com>> wrote: Yep, I already found it. So I added 1 line: val graph = GraphLoader.edgeListFile(sc, "....", ...) val newgraph = graph.convertToCanonicalEdges() and could successfully count triangles on "newgraph". Next will test it on bigger (several Gb) networks. I am using Spark 1.3 and 1.4 but haven't seen this function in https://spark.apache.org/docs/latest/graphx-programming-guide.html Thanks a lot guys! Am 26.06.2015 13:50 schrieb "Ted Yu" <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>>: See SPARK-4917 which went into Spark 1.3.0 On Fri, Jun 26, 2015 at 2:27 AM, Robin East <robin.e...@xense.co.uk<mailto:robin.e...@xense.co.uk>> wrote: You’ll get this issue if you just take the first 2000 lines of that file. The problem is triangleCount() expects srdId < dstId which is not the case in the file (e.g. vertex 28). You can get round this by calling graph.convertToCanonical Edges() which removes bi-directional edges and ensures srcId < dstId. Which version of Spark are you on? Can’t remember what version that method was introduced in. Robin On 26 Jun 2015, at 09:44, Roman Sokolov <ole...@gmail.com<mailto:ole...@gmail.com>> wrote: Ok, but what does it means? I did not change the core files of spark, so is it a bug there? PS: on small datasets (<500 Mb) I have no problem. Am 25.06.2015 18:02 schrieb "Ted Yu" <yuzhih...@gmail.com<mailto:yuzhih...@gmail.com>>: The assertion failure from TriangleCount.scala corresponds with the following lines: g.outerJoinVertices(counters) { (vid, _, optCounter: Option[Int]) => val dblCount = optCounter.getOrElse(0) // double count should be even (divisible by two) assert((dblCount & 1) == 0) Cheers On Thu, Jun 25, 2015 at 6:20 AM, Roman Sokolov <ole...@gmail.com<mailto:ole...@gmail.com>> wrote: Hello! I am trying to compute number of triangles with GraphX. But get memory error or heap size, even though the dataset is very small (1Gb). I run the code in spark-shell, having 16Gb RAM machine (also tried with 2 workers on separate machines 8Gb RAM each). So I have 15x more memory than the dataset size is, but it is not enough. What should I do with terabytes sized datasets? How do people process it? Read a lot of documentation and 2 Spark books, and still have no clue :( Tried to run with the options, no effect: ./bin/spark-shell --executor-memory 6g --driver-memory 9g --total-executor-cores 100 The code is simple: val graph = GraphLoader.edgeListFile(sc, "/home/ubuntu/data/soc-LiveJournal1/lj.stdout", edgeStorageLevel = StorageLevel.MEMORY_AND_DISK_SER, vertexStorageLevel = StorageLevel.MEMORY_AND_DISK_SER).partitionBy(PartitionStrategy.RandomVertexCut) println(graph.numEdges) println(graph.numVertices) val triangleNum = graph.triangleCount().vertices.map(x => x._2).reduce(_ + _)/3 (dataset is from here: http://konect.uni-koblenz.de/downloads/tsv/soc-LiveJournal1.tar.bz2 first two lines contain % characters, so have to be removed). UPD: today tried on 32Gb machine (from spark shell again), now got another error: [Stage 8:> (0 + 4) / 32]15/06/25 13:03:05 WARN ShippableVertexPartitionOps: Joining two VertexPartitions with different indexes is slow. 15/06/25 13:03:05 ERROR Executor: Exception in task 3.0 in stage 8.0 (TID 227) java.lang.AssertionError: assertion failed at scala.Predef$.assert(Predef.scala:165) at org.apache.spark.graphx.lib.TriangleCount$$anonfun$7.apply(TriangleCount.scala:90) at org.apache.spark.graphx.lib.TriangleCount$$anonfun$7.apply(TriangleCount.scala:87) at org.apache.spark.graphx.impl.VertexPartitionBaseOps.leftJoin(VertexPartitionBaseOps.scala:140) at org.apache.spark.graphx.impl.VertexPartitionBaseOps.leftJoin(VertexPartitionBaseOps.scala:133) at org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:159) at org.apache.spark.graphx.impl.VertexRDDImpl$$anonfun$3.apply(VertexRDDImpl.scala:156) at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:88) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.graphx.VertexRDD.compute(VertexRDD.scala:71) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277) at org.apache.spark.rdd.RDD.iterator(RDD.scala:244) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63) at org.apache.spark.scheduler.Task.run(Task.scala:70) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) -- Best regards, Roman Sokolov -- Best regards, Roman Sokolov